Brucellosis, a zoonosis which can seriously harm public health. Inner Mongolia is known as an endemic region of human brucellosis in China. The aim of this study is to present the epidemic of human brucellosis in Inner Mongolia from 2010 to 2014, and to provide the evidence of preventing from brucellosis in endemic areas. The study included 838,956 participants by cluster random sampling from 2010 to 2014. The contents of this study included questionnaire survey and serological testing. The seropositive rate was calculated based on the serological testing results, tested to determine whether the participants were infected by brucellosis. The new brucellosis cases were diagnosed using the questionnaire data and serological testing results then and the incidence rate was obtained. We also plotted spatial distribution maps based on the seropositive and incidence rate of human brucellosis. The total seropositive and incidence rate of human brucellosis among the high risk groups in Inner Mongolia from 2010 to 2014 was 35.91‰ and 18.25‰, respectively. The epidemic of the mid-eastern Inner Mongolia was decreased, however, the epidemic has worsened in western Inner Mongolia. Despite the epidemic of human brucellosis among the high risk groups of Inner Mongolia decreased significantly from 2010 to 2014, increased prevention and control measures are urgently needed.

At present, the rate control algorithm for multiview high-efficiency video coding (MV-HEVC) does not have the capability of efficient coding tree unit(CTU) layer bit allocation, and the video quality varies greatly for sequences with sudden scene changes or large motions. To overcome this limitation, this paper proposes a rate control algorithm for MV-HEVC based on scene detection. Firstly, we established ρ domain rate control model based on multi-objective optimization. Then, it uses image similarity to make reasonable bit allocation among viewpoints. If the video scene is switched, the image similarity is recalculated, and then the correlation between the weights of the interview point rates and the correlation between the viewpoints are analyzed. Finally, the frame layer rate control considers the layer B-frame and other factors in allocating the code rate, and the basic unit layer rate control adopts different quantization methods according to the content complexity of the CTU. Experimental results show that the proposed rate control algorithm can maintain good coding efficiency and decrease the average video quality variation by 25.29%.

Light field imaging technology captures a wider view of the scene light information and has become a mainstream method to broaden the scope of camera views. Unlike traditional images, light field images have a more complex structure. This complexity arises from the need to ensure the consistency of the internal structure of the light field image and address the occlusion between adjacent images during the reconstruction process.^{1 – 3} Therefore, conducting extensive research on the structural properties of light field images, advancing technologies for occlusion removal, and developing efficient reconstruction methods are essential for the widespread adoption of light field imaging. These efforts will ensure that light field imaging can be effectively applied in various fields, such as virtual reality, augmented reality, and computational photography.

Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.

Most existing rate control algorithms are based on the rate-quantization (R-Q) model. However, with video coding schemes becoming more flexible, it is very difficult to accurately model the R-Q relationship. Therefore, in this study we propose a novel ρ domain rate control algorithm for multiview high efficiency video coding (MV-HEVC). Firstly, in order to further improve the efficiency of MV-HEVC, this paper uses our previous research algorithm to optimize the MV-HEVC prediction structure. Then, we established the ρ domain rate control model based on multi-objective optimization. Finally, it used image similarity to analyze the correlation between viewpoints, using encoded information and frame complexity to proceed in bit allocation and bit rate control of the inter-view, frame lay, and base unit. The experimental simulation results show that the algorithm can simultaneously maintain high coding efficiency, where the average error of the actual bit rate and the target bit rate is only 0.9%.